Future Reliability Prediction Based on System Operational and Performance Data Modelling

1. A method, comprising: receiving, by at least one processor, operating data associated with a facility in real-time, wherein the operating data comprises one or more operating characteristics of a measurable system for the facility; receiving, by the at least one processor, asset reliability data associated with the facility; utilizing, by the at least one processor, one or more machine learning models to optimize performance of operation activities within the facility based on an identification of a target variable associated with the facility in comparison to the operating data and the asset reliability data; generating, by the at least one processor, an operation standard associated with the facility to form a plurality of category values based on the target variable associated with the performance of the operation activities, wherein the plurality of category values categorize the operation data by a predetermined interval based on the operation data, the target variable and a result of the one or more machine learning models; determining, by the at least one processor, an estimated future reliability of the facility based on the asset reliability data and the plurality of category values for subsequent updates associated with current data of the facility to form an updated estimated future reliability of the facility; continually tracking, by the at least one processor, the plurality of category values associated with the target variable of the facility in real-time; and causing to display, by the at least one processor, information regarding the estimated future reliability of the facility and/or information regarding the updated estimated future reliability of the facility.

2. The computer-implemented method of claim 1, wherein the the utilizing of the one or more machine learning modules to optimize the performance of the operation activities further comprising: determining a set of first improvement error values for the operating data of the facility; determining a set of second improvement error values for the current data of the facility; and comparing the set of first improvement error values with the set of second improvement error values to identify a trend between both sets of error values.

3. The computer-implemented method of claim 2, wherein one or more of the set of first improvement error values are standard error values.

4. The computer-implemented method of claim 2, wherein one or more of the set of first improvement error values are coefficient of determination values.

5. The computer-implemented method of claim 1, wherein the plurality of category values comprises a set of relative error values and a set of absolute error values.

6. The computer-implemented method of claim 5, wherein the plurality of category values are determined for one or more different facilities that have the relative error values and the absolute error values for respective operating data exceeding a predetermined error threshold.

7. The computer-implemented method of claim 1, further comprising: recombining operating data of the facility with the current data of to produce the updated operating data.

8. The method of claim 1, further comprising compiling the operation standard and the asset reliability data into a compiled data file.

9. The method of claim 8, further comprising: generating a categorized time-based operation data based on at least the compiled data file; and generating a categorized time-based asset reliability data based on at least the complied data file.

10. The method of claim 9, further comprising: generating the categorized time-based operation data by arranging the plurality of category values according to one or more time intervals for the facility.

11. The method of claim 9, further comprising: Generating the categorized time-based asset reliability data by arranging asset reliability data values according to one or more time intervals for the facility.

12. The method of claim 1, wherein a future reliability interval of the estimated future reliability is based on an amount of the operation data, the asset reliability data, and the current data.

13. The method of claim 1, wherein the operation standard is utilized to normalize the operation data.

14. The method of claim 13, further comprising generating a periodic operating performance divisor for a predetermined period of time to normalize the operation data.

15. A computer system, comprising: at least one server, comprising: at least one processor and a non-transient storage subsystem; wherein the non-transient storage subsystem stores a computer program comprising instructions that, when executed by the at least one processor, cause the at least one processor to at least: receive operating data associated with a facility in real-time, wherein the operating data comprises one or more operating characteristics of a measurable system for the facility; receive asset reliability data associated with the facility; utilize one or more machine learning models to optimize performance of operation activities within the facility based on an identification of a target variable associated with the facility in comparison to the operating data and the asset reliability data; generate an operation standard associated with the facility to form a plurality of category values based on the target variable associated with the performance of the operation activities, wherein the plurality of category values categorize the operation data by a predetermined interval based on the operation data, the target variable and a result of the one or more machine learning models; determine an estimated future reliability of the facility based on the asset reliability data and the plurality of category values for subsequent updates associated with current data of the facility to form an updated estimated future reliability of the facility; continually track the plurality of category values associated with the target variable of the facility in real-time; and a user interface configured to display information regarding the estimated future reliability of the facility and/or information regarding the updated estimated future reliability of the facility.

16. The system of claim 15, wherein the instructions of the computer program further comprise: recombining operating data of the facility with the current data to produce updated operating data.

17. The system of claim 15, wherein the utilization of the one or more machine learning models to optimize performance of the operation activities further comprises: determining a set of first improvement error values for the operating data of the facility; determining a set of second improvement error values for the current data of the facility; and comparing the set of first improvement error values with the set of second improvement error values to identify a trend between both sets of error values.

18. The system of claim 17, wherein one or more of the set of first improvement error values are standard error values.

19. The system of claim 17, wherein one or more the set of first improvement values are coefficient of determination values.